OpenRaiser/Intern-Atlas huggingface dataset:
MATURE-HEALTH: HEALTH Recommender System for MAndatory FeaTURE choices
Venue: arXiv cs.IR 2023
Year: 2023

Balancing electrolytes is utmost important and essential for appropriate functioning of organs in human body as electrolytes imbalance can be an indication of the development of underlying pathophysiology. Efficient monitoring of electrolytes imbalance not only can increase the chances of early detection of disease, but also prevents the further deterioration of the health by strictly following nutrient controlled diet for balancing the electrolytes post disease detection. In this research, a recommender system MATURE Health is proposed and implemented, which predicts the imbalance of mandatory electrolytes and other substances presented in blood and recommends the food items with the balanced nutrients to avoid occurrence of the electrolytes imbalance. The proposed model takes user most recent laboratory results and daily food intake into account to predict the electrolytes imbalance. MATURE Health relies on MATURE Food algorithm to recommend food items as latter recommends only those food items that satisfy all mandatory nutrient requirements while also considering user past food preferences. To validate the proposed method, particularly sodium, potassium, and BUN levels have been predicted with prediction algorithm, Random Forest, for dialysis patients using their laboratory reports history and daily food intake. And, the proposed model demonstrates 99.53 percent, 96.94 percent and 95.35 percent accuracy for Sodium, Potassium, and BUN respectively. MATURE Health is a novel health recommender system that implements machine learning models to predict the imbalance of mandatory electrolytes and other substances in the blood and recommends the food items which contain the required amount of the nutrients that prevent or at least reduce the risk of the electrolytes imbalance.
IMoG -- a methodology for modeling future microelectronic innovations
Venue: arXiv cs.SI 2023
Year: 2023

[Context and motivation] The automotive industry is currently undergoing a fundamental transformation towards software defined vehicles. The automotive market of the future demands a higher level of automation, electrification of the power train, and individually configurable comfort functions. [Question/problem] These demands pose a challenge to the automotive development cycle, because they introduce complexity by larger and not yet well explored design spaces that are difficult to manage. [Principal ideas/results] To cope with these challenges, the main players along the value chain have an increased interest in collaborating and aligning their development efforts along joint roadmaps. Roadmap development can be viewed as a field of requirements engineering with the goal to capture product aspects on an appropriate level of abstraction to speed up investment decisions, reduce communication overhead and parallelize development activities, while complying with competition laws. [Contribution] In this paper, we present a refinement of the "Innovation Modeling Grid" (IMoG), which encompasses a methodology, a process and a proposed notation to support joint analysis of development roadmaps. IMoG is focused on the automotive domain, yet there are clear potentials for other applications.
Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task
Venue: arXiv cs.CL 2023
Year: 2023

Recently, ChatGPT and GPT-4 have emerged and gained immense global attention due to their unparalleled performance in language processing. Despite demonstrating impressive capability in various open-domain tasks, their adequacy in highly specific fields like radiology remains untested. Radiology presents unique linguistic phenomena distinct from open-domain data due to its specificity and complexity. Assessing the performance of large language models (LLMs) in such specific domains is crucial not only for a thorough evaluation of their overall performance but also for providing valuable insights into future model design directions: whether model design should be generic or domain-specific. To this end, in this study, we evaluate the performance of ChatGPT/GPT-4 on a radiology NLI task and compare it to other models fine-tuned specifically on task-related data samples. We also conduct a comprehensive investigation on ChatGPT/GPT-4's reasoning ability by introducing varying levels of inference difficulty. Our results show that 1) GPT-4 outperforms ChatGPT in the radiology NLI task; 2) other specifically fine-tuned models require significant amounts of data samples to achieve comparable performance to ChatGPT/GPT-4. These findings demonstrate that constructing a generic model that is capable of solving various tasks across different domains is feasible.
Using simulation to design an MPC policy for field navigation using GPS sensing
Venue: arXiv cs.RO 2023
Year: 2023

Modeling a robust control system with a precise GPS-based state estimation capability in simulation can be useful in field navigation applications as it allows for testing and validation in a controlled environment. This testing process would enable navigation systems to be developed and optimized in simulation with direct transferability to real-world scenarios. The multi-physics simulation engine Chrono allows for the creation of scenarios that may be difficult or dangerous to replicate in the field, such as extreme weather or terrain conditions. Autonomy Research Testbed (ART), a specialized robotics algorithm testbed, is operated in conjunction with Chrono to develop an MPC control policy as well as an EKF state estimator. This platform enables users to easily integrate custom algorithms in the autonomy stack. This model is initially developed and used in simulation and then tested on a twin vehicle model in reality, to demonstrate the transferability between simulation and reality (also known as Sim2Real).
Memento: Facilitating Effortless, Efficient, and Reliable ML Experiments
Venue: arXiv cs.LG 2023
Year: 2023

Running complex sets of machine learning experiments is challenging and time-consuming due to the lack of a unified framework. This leaves researchers forced to spend time implementing necessary features such as parallelization, caching, and checkpointing themselves instead of focussing on their project. To simplify the process, in this paper, we introduce Memento, a Python package that is designed to aid researchers and data scientists in the efficient management and execution of computationally intensive experiments. Memento has the capacity to streamline any experimental pipeline by providing a straightforward configuration matrix and the ability to concurrently run experiments across multiple threads. A demonstration of Memento is available at: https://wickerlab.org/publication/memento.
New Subset Selection Algorithms for Low Rank Approximation: Offline and Online
Venue: arXiv cs.DS 2023
Year: 2023

Subset selection for the rank $k$ approximation of an $n\times d$ matrix $A$ offers improvements in the interpretability of matrices, as well as a variety of computational savings. This problem is well-understood when the error measure is the Frobenius norm, with various tight algorithms known even in challenging models such as the online model, where an algorithm must select the column subset irrevocably when the columns arrive one by one. In contrast, for other matrix losses, optimal trade-offs between the subset size and approximation quality have not been settled, even in the offline setting. We give a number of results towards closing these gaps. In the offline setting, we achieve nearly optimal bicriteria algorithms in two settings. First, we remove a $\sqrt k$ factor from a result of [SWZ19] when the loss function is any entrywise loss with an approximate triangle inequality and at least linear growth. Our result is tight for the $\ell_1$ loss. We give a similar improvement for entrywise $\ell_p$ losses for $p>2$, improving a previous distortion of $k^{1-1/p}$ to $k^{1/2-1/p}$. Our results come from a technique which replaces the use of a well-conditioned basis with a slightly larger spanning set for which any vector can be expressed as a linear combination with small Euclidean norm. We show that this technique also gives the first oblivious $\ell_p$ subspace embeddings for $1<p<2$ with $\tilde O(d^{1/p})$ distortion, which is nearly optimal and closes a long line of work. In the online setting, we give the first online subset selection algorithm for $\ell_p$ subspace approximation and entrywise $\ell_p$ low rank approximation by implementing sensitivity sampling online, which is challenging due to the sequential nature of sensitivity sampling. Our main technique is an online algorithm for detecting when an approximately optimal subspace changes substantially.
A Framework for Analyzing Cross-correlators using Price's Theorem and Piecewise-Linear Decomposition
Venue: arXiv cs.LG 2023
Year: 2023

Precise estimation of cross-correlation or similarity between two random variables lies at the heart of signal detection, hyperdimensional computing, associative memories, and neural networks. Although a vast literature exists on different methods for estimating cross-correlations, the question what is the best and simplest method to estimate cross-correlations using finite samples ? is still unclear. In this paper, we first argue that the standard empirical approach might not be the optimal method even though the estimator exhibits uniform convergence to the true cross-correlation. Instead, we show that there exists a large class of simple non-linear functions that can be used to construct cross-correlators with a higher signal-to-noise ratio (SNR). To demonstrate this, we first present a general mathematical framework using Price's Theorem that allows us to analyze cross-correlators constructed using a mixture of piece-wise linear functions. Using this framework and high-dimensional embedding, we show that some of the most promising cross-correlators are based on Huber's loss functions, margin-propagation (MP) functions, and the log-sum-exp (LSE) functions.
Hybrid Task Constrained Planner for Robot Manipulator in Confined Environment
Venue: arXiv cs.RO 2023
Year: 2023

Trajectory generation in confined environment is crucial for wide adoption of intelligent robot manipulators. In this paper, we propose a novel motion planning approach for redundant robot arms that uses a hybrid optimization framework to search for optimal trajectories in both the configuration space and null space, generating high-quality trajectories that satisfy task constraints and collision avoidance constraints, while also avoiding local optima for incremental planners. Our approach is evaluated in an onsite polishing scenario with various robot and workpiece configurations, demonstrating significant improvements in trajectory quality compared to existing methods. The proposed approach has the potential for broad applications in industrial tasks involving redundant robot arms.
Optimal Eigenvalue Approximation via Sketching
Venue: arXiv cs.DS 2023
Year: 2023

Given a symmetric matrix $A$, we show from the simple sketch $GAG^T$, where $G$ is a Gaussian matrix with $k = O(1/ε^2)$ rows, that there is a procedure for approximating all eigenvalues of $A$ simultaneously to within $ε\|A\|_F$ additive error with large probability. Unlike the work of (Andoni, Nguyen, SODA, 2013), we do not require that $A$ is positive semidefinite and therefore we can recover sign information about the spectrum as well. Our result also significantly improves upon the sketching dimension of recent work for this problem (Needell, Swartworth, Woodruff FOCS 2022), and in fact gives optimal sketching dimension. Our proof develops new properties of singular values of $GA$ for a $k \times n$ Gaussian matrix $G$ and an $n \times n$ matrix $A$ which may be of independent interest. Additionally we achieve tight bounds in terms of matrix-vector queries. Our sketch can be computed using $O(1/ε^2)$ matrix-vector multiplies, and by improving on lower bounds for the so-called rank estimation problem, we show that this number is optimal even for adaptive matrix-vector queries.
AI-based Agents for Automated Robotic Endovascular Guidewire Manipulation
Venue: arXiv cs.RO 2023
Year: 2023

Endovascular guidewire manipulation is essential for minimally-invasive clinical applications (Percutaneous Coronary Intervention (PCI), Mechanical thrombectomy techniques for acute ischemic stroke (AIS), or Transjugular intrahepatic portosystemic shunt (TIPS)). All procedures commonly require 3D vessel geometries from 3D CTA (Computed Tomography Angiography) images. During these procedures, the clinician generally places a guiding catheter in the ostium of the relevant vessel and then manipulates a wire through the catheter and across the blockage. The clinician only uses X-ray fluoroscopy intermittently to visualize and guide the catheter, guidewire, and other devices. However, clinicians still passively control guidewires/catheters by relying on limited indirect observation (i.e., 2D partial view of devices, and intermittent updates due to radiation limit) from X-ray fluoroscopy. Modeling and controlling the guidewire manipulation in coronary vessels remains challenging because of the complicated interaction between guidewire motions with different physical properties (i.e., loads, coating) and vessel geometries with lumen conditions resulting in a highly non-linear system. This paper introduces a scalable learning pipeline to train AI-based agent models toward automated endovascular predictive device controls. First, we create a scalable environment by pre-processing 3D CTA images, providing patient-specific 3D vessel geometry and the centerline of the coronary. Next, we apply a large quantity of randomly generated motion sequences from the proximal end to generate wire states associated with each environment using a physics-based device simulator. Then, we reformulate the control problem to a sequence-to-sequence learning problem, in which we use a Transformer-based model, trained to handle non-linear sequential forward/inverse transition functions.
Integrity and Junkiness Failure Handling for Embedding-based Retrieval: A Case Study in Social Network Search
Venue: arXiv cs.IR 2023
Year: 2023

Embedding based retrieval has seen its usage in a variety of search applications like e-commerce, social networking search etc. While the approach has demonstrated its efficacy in tasks like semantic matching and contextual search, it is plagued by the problem of uncontrollable relevance. In this paper, we conduct an analysis of embedding-based retrieval launched in early 2021 on our social network search engine, and define two main categories of failures introduced by it, integrity and junkiness. The former refers to issues such as hate speech and offensive content that can severely harm user experience, while the latter includes irrelevant results like fuzzy text matching or language mismatches. Efficient methods during model inference are further proposed to resolve the issue, including indexing treatments and targeted user cohort treatments, etc. Though being simple, we show the methods have good offline NDCG and online A/B tests metrics gain in practice. We analyze the reasons for the improvements, pointing out that our methods are only preliminary attempts to this important but challenging problem. We put forward potential future directions to explore.
Towards Spatio-temporal Sea Surface Temperature Forecasting via Static and Dynamic Learnable Personalized Graph Convolution Network
Venue: arXiv cs.LG 2023
Year: 2023

Sea surface temperature (SST) is uniquely important to the Earth's atmosphere since its dynamics are a major force in shaping local and global climate and profoundly affect our ecosystems. Accurate forecasting of SST brings significant economic and social implications, for example, better preparation for extreme weather such as severe droughts or tropical cyclones months ahead. However, such a task faces unique challenges due to the intrinsic complexity and uncertainty of ocean systems. Recently, deep learning techniques, such as graphical neural networks (GNN), have been applied to address this task. Even though these methods have some success, they frequently have serious drawbacks when it comes to investigating dynamic spatiotemporal dependencies between signals. To solve this problem, this paper proposes a novel static and dynamic learnable personalized graph convolution network (SD-LPGC). Specifically, two graph learning layers are first constructed to respectively model the stable long-term and short-term evolutionary patterns hidden in the multivariate SST signals. Then, a learnable personalized convolution layer is designed to fuse this information. Our experiments on real SST datasets demonstrate the state-of-the-art performances of the proposed approach on the forecasting task.
Virtual Fidgets: Opportunities and Design Principles for Bringing Fidgeting to Online Learning
Venue: arXiv cs.HC 2023
Year: 2023

We present design guidelines for incorporating fidgeting into the virtual world as a tool for students in online lectures. Fidgeting is associated with increased attention and self-regulation, and has the potential to help students focus. Currently there are no fidgets, physical or virtual, designed for preserving attention specifically in online learning environments, and no heuristics for designing fidgets within this domain. We identify three virtual fidget proxies to serve as design probes for studying student experiences with virtual fidgeting. Through a study of eight students using our virtual fidget proxies in online lectures, we identify eight emergent themes that encompass student experience with virtual fidgeting in lectures. Based on these themes, we present four principles for designing domain-specific virtual fidgets for online lectures. We identify that virtual fidgets for lectures should be context-aware, visually appealing, easy to adopt, and physically interactive.
Application of genetic algorithm to load balancing in networks with a homogeneous traffic flow
Venue: arXiv cs.NI 2023
Year: 2023

The concept of extended cloud requires efficient network infrastructure to support ecosystems reaching form the edge to the cloud(s). Standard approaches to network load balancing deliver static solutions that are insufficient for the extended clouds, where network loads change often. To address this issue, a genetic algorithm based load optimizer is proposed and implemented. Next, its performance is experimentally evaluated and it is shown that it outperforms other existing solutions.
BioThings Explorer: a query engine for a federated knowledge graph of biomedical APIs
Venue: arXiv cs.DB 2023
Year: 2023

Knowledge graphs are an increasingly common data structure for representing biomedical information. These knowledge graphs can easily represent heterogeneous types of information, and many algorithms and tools exist for querying and analyzing graphs. Biomedical knowledge graphs have been used in a variety of applications, including drug repurposing, identification of drug targets, prediction of drug side effects, and clinical decision support. Typically, knowledge graphs are constructed by centralization and integration of data from multiple disparate sources. Here, we describe BioThings Explorer, an application that can query a virtual, federated knowledge graph derived from the aggregated information in a network of biomedical web services. BioThings Explorer leverages semantically precise annotations of the inputs and outputs for each resource, and automates the chaining of web service calls to execute multi-step graph queries. Because there is no large, centralized knowledge graph to maintain, BioThing Explorer is distributed as a lightweight application that dynamically retrieves information at query time. More information can be found at https://explorer.biothings.io, and code is available at https://github.com/biothings/biothings_explorer.
Machine Vision System for Early-stage Apple Flowers and Flower Clusters Detection for Precision Thinning and Pollination
Venue: arXiv cs.CV 2023
Year: 2023

Early-stage identification of fruit flowers that are in both opened and unopened condition in an orchard environment is significant information to perform crop load management operations such as flower thinning and pollination using automated and robotic platforms. These operations are important in tree-fruit agriculture to enhance fruit quality, manage crop load, and enhance the overall profit. The recent development in agricultural automation suggests that this can be done using robotics which includes machine vision technology. In this article, we proposed a vision system that detects early-stage flowers in an unstructured orchard environment using YOLOv5 object detection algorithm. For the robotics implementation, the position of a cluster of the flower blossom is important to navigate the robot and the end effector. The centroid of individual flowers (both open and unopen) was identified and associated with flower clusters via K-means clustering. The accuracy of the opened and unopened flower detection is achieved up to mAP of 81.9% in commercial orchard images.
Inferring High-level Geographical Concepts via Knowledge Graph and Multi-scale Data Integration: A Case Study of C-shaped Building Pattern Recognition
Venue: arXiv cs.CV 2023
Year: 2023

Effective building pattern recognition is critical for understanding urban form, automating map generalization, and visualizing 3D city models. Most existing studies use object-independent methods based on visual perception rules and proximity graph models to extract patterns. However, because human vision is a part-based system, pattern recognition may require decomposing shapes into parts or grouping them into clusters. Existing methods may not recognize all visually aware patterns, and the proximity graph model can be inefficient. To improve efficiency and effectiveness, we integrate multi-scale data using a knowledge graph, focusing on the recognition of C-shaped building patterns. First, we use a property graph to represent the relationships between buildings within and across different scales involved in C-shaped building pattern recognition. Next, we store this knowledge graph in a graph database and convert the rules for C-shaped pattern recognition and enrichment into query conditions. Finally, we recognize and enrich C-shaped building patterns using rule-based reasoning in the built knowledge graph. We verify the effectiveness of our method using multi-scale data with three levels of detail (LODs) collected from the Gaode Map. Our results show that our method achieves a higher recall rate of 26.4% for LOD1, 20.0% for LOD2, and 9.1% for LOD3 compared to existing approaches. We also achieve recognition efficiency improvements of 0.91, 1.37, and 9.35 times, respectively.